Evidence-Aware Predictive Delivery and Continuous Software
Assurance for Regulated Energy Utility Platforms

This study developed and evaluated an evidence-aware predictive delivery and continuous software-assurance framework for regulated energy utility platforms. The framework integrates a multi-signal release-risk estimate with machine-verifiable evidence completeness, rollback readiness, mandatory policy state, utility operating sensitivity, and deterministic risk-tiered routing. Real ISO-NE observations derived from EIA-930 provide the operating context, while reproducible synthetic release records supply software-delivery signals and outcomes that are unavailable in public utility datasets. The architecture therefore connects software risk and utility context without treating grid observations as software-failure labels. The empirical results show that learned multi-signal release-risk modeling improves discrimination over static governance. The proposed framework reaches ROC-AUC 0.810 and F1 0.602; the external predictive-delivery benchmark [5] reaches 0.805 and 0.600. Evidence ablation produces 68.6 percent policy escape, while the full framework maintains 0.0 percent through deterministic enforcement. Routing becomes more conservative as utility operating sensitivity rises, placing the principal contribution in evidence-aware, context-sensitive assurance rather than marginal predictive lift. Real utility CI/CD validation remains necessary before the framework can be claimed as validated for production utility delivery. Future work should replace synthetic release telemetry with de-identified CI/CD, deployment, incident, rollback, and evidence records from operating utilities. A shadow-mode validation should score real releases and produce recommended routes without authorizing production deployment. Such a study should measure calibration under actual failure labels, route accuracy, operator trust, override behavior, post-release health, evidence-generation reliability, and whether context-aware routing improves decisions during both normal and operationally sensitive periods.

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